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The Portable Format for Analytics (PFA) is a JSON-based predictive model interchange format conceived and developed by Jim Pivarski. [ citation needed ] PFA provides a way for analytic applications to describe and exchange predictive models produced by analytics and machine learning algorithms.
Notes Reference Java: The Java API is implemented using JNI. [20] Integration with the Apache Arrow [21] format is provided. [22] Python: The Python API implements support for the Pandas, [23] Apache Arrow [24] and Polars data analysis packages. [25] Rust: The Rust API is distributed as a rust crate that exposes an elegant wrapper over the ...
Wes McKinney is an American software developer and businessman. He is the creator and "Benevolent Dictator for Life" (BDFL) of the open-source pandas package for data analysis in the Python programming language, and has also authored three versions of the reference book Python for Data Analysis.
If data is a Series, then data['a'] returns all values with the index value of a. However, if data is a DataFrame, then data['a'] returns all values in the column(s) named a. To avoid this ambiguity, Pandas supports the syntax data.loc['a'] as an alternative way to filter using the index.
Orange – A visual programming tool featuring interactive data visualization and methods for statistical data analysis, data mining, and machine learning. Pandas – Python library for data analysis. PAW – FORTRAN/C data analysis framework developed at CERN. R – A programming language and software environment for statistical computing and ...
Plotly is a technical computing company headquartered in Montreal, Quebec, that develops online data analytics and visualization tools. Plotly provides online graphing, analytics, and statistics tools for individuals and collaboration, as well as scientific graphing libraries for Python, R, MATLAB, Perl, Julia, Arduino, JavaScript [1] and REST.
From January 2008 to December 2012, if you bought shares in companies when J. Taft Symonds joined the board, and sold them when he left, you would have a 73.5 percent return on your investment, compared to a -2.8 percent return from the S&P 500.
In summary, data analysis and data science are distinct yet interconnected disciplines within the broader field of data management and analysis. Data analysis focuses on extracting insights and drawing conclusions from structured data, while data science involves a more comprehensive approach that combines statistical analysis, computational ...